Multiscale network complementarity
Disease modules, the hallmark concept for disease gene localization in network medicine, have been extensively studied on the PPI network. In this study, we expanded this concept across scales by incorporating additional databases that were selected to represent the multiscale biological relationships from genotypes to phenotypes. These multiscale network construction from databases of various formats including the bipartite mapping (e.g. the gene-pathway association), the ontology-based semantic similarity measurement (e.g. the Gene Ontology annotation), and correlation-based relationship extracted from quantitative studies such as GTEx expression data. These networks are complementary on different levels.
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knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
library(pacman)
p_load(patchwork, igraph, tidyverse, cowplot, rmarkdown)
# compute different network properties
if(!file.exists("../cache/network_complementarity_topological.RDS")){
# load required functions
source("../source/network_properties_analysis.R")
} else{
print("Load precomputed data")
g_prop_df <- readRDS("../cache/network_complementarity_topological.RDS")
}
[1] "Load precomputed data"Show code
network_details <- read_tsv("../data/network_details.tsv", col_types = 'ccccc')
Network details
45 Network layers from six major databases were constructed as detailed below:
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paged_table(network_details %>% select(!type))
Topological complementarity
In addition to the scale comprehensiveness, these networks are also topologically complementary. A number of key network properties including node and link coverage, modularity, assortativity, and social bias, have been compared and shown below.
Social bias: many networks were constructed based on curation from literatures. The social bias of a network is assessed by the Spearman’s correlation coefficient between the network degree of a gene and the number of publications mentioning the gene. The number of publications was queried using the INDRA python module (http://www.indra.bio, accessed on 12 April 2019)
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Note that, for the co-expression layer, the values showed on the table above are averaged from all 38 tissue-specific networks.
The plot below summarises the table properties.
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# create a list of plots to patch together
plot = list()
for(prop in unique(g_prop_df$property)){
plot[[prop]] = g_prop_df %>%
arrange(group) %>% filter(property == prop) %>%
ggplot( aes(x=group, y=as.numeric(value))) +
geom_segment( aes(x=group, xend=group, y=0, yend=value), color="grey80", size=1.5) +
geom_violin(fill="#F8B100", alpha = 0.4, color = NA) +
geom_point( aes(color=alphaval), size=4, alpha=0.6) +
theme_light() +
coord_flip() +
theme(
panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
# axis.text.y = element_blank(),
) +
guides(color = F)+
xlab("") +
scale_color_manual(values = c("#F8B100",NA)) +
ylab(prop)
# scale y log for some properties (n edges)
if(prop %in% c("Number of edges")){
plot[[prop]] = plot[[prop]] + scale_y_log10()
}
# for the first plot, allows axis label
if(!prop %in% c("Number of nodes")){
plot[[prop]] = plot[[prop]] + theme(axis.text.y = element_blank())
}
}
plot_combine = plot$`Number of nodes` + plot$`Edge density` + plot$`Global clustering` + plot$Assortativity + plot$`Social bias` + plot_layout(nrow = 1)
# uncomment to save the plot as pdf
#ggsave("../Figs/network_properties_characterisation.pdf", plot_combine, height = 2.5, width = 9)
suppressWarnings(print(plot_combine))
The Social bias of the networks
The network similarity
We quantified the similarity of a given pair of networks \(g_A \in G(V_A, E_A)\) and \(g_B \in G(V_A, E_A)\) using the edge overlap index: \[S_{AB}=\dfrac{|E_A \cap E_B|}{\text{min}(|E_A|,|E_B|)}\] We used a dissimilarity measure defined as \(d_{AB} = 1 - S_{AB}\) to construct a 2D map \(\mathbf{X} \subset \mathbb{R}^{2}\) that preserves network dissimilarities by employing Kruskal’s non-metric multidimensional scaling (R package MASS) 75. Finally, we compared the measured similarity of each network pair to random expectation: For each network, we performed 10 permutations of node indices, resulting in 100 permutations for a network pair which we used as random reference distribution to assess the measured overlap similarity. We then computed \(z\)-score and the corresponding empirical \(p\)-value. A network pair with \(p-\)value < 0.05 is considered significant.
The MDS plot derived from Jaccard and Overlap Similarity is as follows:
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pacman::p_load(ggrepel, MASS)
# load the precomputed data
if(!file.exists("../cache/network_jaccard_overlap_similarity_df.RDS")){
source("../source/compute_jaccard_similarity.R")
} else{
print("load pre-computed network similarity data")
network_sim_df <- readRDS("../cache/network_jaccard_overlap_similarity_df.RDS")
}
[1] "load pre-computed network similarity data"Show code
# turn df to weight symmatrix matrix through graph
g_overlap <- graph_from_data_frame(network_sim_df[,c(1,2,4)] %>% rename(., weight = overlapindex), directed = F)
sim_overlap <- get.adjacency(g_overlap, attr = "weight")
diag(sim_overlap) = 1
#change similarity to distance
dist_overlap = 1 - sim_overlap
############
# MDS plot normal
#### MDS plot for Kruskal
mds<- isoMDS(as.matrix(dist_overlap), k = 2)
initial value 46.790384
iter 5 value 32.191184
iter 10 value 23.308862
iter 15 value 21.770579
final value 21.704111
convergedShow code
# a data frame of MDS values
mds_df = data.frame(x = mds$points[,1], y = mds$points[,2], network = rownames(mds$points))
# add network metadata and node size
mds_df = mds_df %>%
left_join(., g_prop_df %>% dplyr::filter(property=="Number of nodes") %>% select(network, value)) %>%
left_join(., network_details) %>%
dplyr::filter(!is.na(main_type)) %>%
mutate(label = ifelse(!grepl("coex", network), subtype, ""),
# collabel = ifelse(!is.na(type), type, subtype)
)
# plot the scatters of all networks
p <- mds_df %>%
ggplot() +
geom_point(aes(x, y, col = main_type, size = value), alpha = 0.5) +
geom_text_repel(aes(x, y, label = label)) +
theme_cowplot() +theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.ticks.y=element_blank(),
axis.text.y=element_blank()) +
xlab("MDS1") + ylab("MDS2") +
scale_color_manual(values = c("#F8B100", "#005564"))+
guides(col = F, size = F)
p
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#ggsave("../Figs/scatter_Network_complementarity_MDS_Overlap.pdf",plot = p, width = 4, height = 4)
Summary statistics of the network similarity
Overlap index:
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network_sim_df %>% pull(overlapindex) %>% summary
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0001835 0.0121798 0.0331371 0.0419274 0.0543372 0.4899776 Median overlap indices between co-expression networks are 0.0433192 and non co-expression networks are 0.0183954.
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## Jaccard heatmap
## remove coex_core from the map
p1 = network_sim_df[,1:3] %>% dplyr::filter(!grepl("core", V1), !grepl("core", V2)) %>%
# heatmap plot
ggplot() + geom_tile(aes(x = V1, y = V2, fill = jaccardIndex)) + scale_fill_distiller(direction = 1) + xlab("") +ylab("") + ggtitle("Jaccard similarity among all networks") + theme_minimal()+ theme(axis.text.x = element_text(angle = 90))
p1
We use core transcriptional modules to represent all of the co-expression network. The heatmap below shows the Jaccard and Overlap similarity.
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## remove coex_core from the map
considered_networks = c("coex_core", "reactome_copathway", "ppi", "MP", "HP", "GOMF", "GOBP")
labels = c("co-expression", "co-pathway", "PPI", "MP", "HP", "GOMF", "GOBP ")
p1 = network_sim_df[,1:3] %>% dplyr::filter(V1 %in% considered_networks, V2 %in% considered_networks ) %>%
# rescale factor
mutate(V1 = factor(V1, levels = considered_networks, labels = labels),
V2 = factor(V2, levels = considered_networks, labels = labels)) %>%
# heatmap plot
ggplot() + geom_tile(aes(x = V1, y = V2, fill = jaccardIndex)) + scale_fill_distiller(direction = 1) + xlab("") +ylab("") + ggtitle("Jaccard similarity") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
p1
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overlap_df <- network_sim_df[,c(1,2,4)] %>% dplyr::filter(V1 %in% considered_networks, V2 %in% considered_networks ) %>%
# rescale factor
mutate(V1 = factor(V1, levels = considered_networks, labels = labels),
V2 = factor(V2, levels = considered_networks, labels = labels))
# heatmap plot
p2 = ggplot(overlap_df) + geom_tile(aes(x = V1, y = V2, fill = overlapindex)) + scale_fill_distiller(direction = 1) + xlab("") +ylab("") + ggtitle("overlap similarity ") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust=1))
p2
[1] "average overlap index for all networks are 0.060363"Network similairty randomisation
For a given pair of network \(g_A \in G(V_A, E_A)\) and \(g_B \in G(V_A, E_A)\), we computed edge similarity through overlap index:
\[S_{AB}=\dfrac{|E_A \cap E_B|}{\text{min}(|E_A|,|E_B|)}\]
Foe each network, we performed 10 permutations of node indices, resulting in 100 permutations for a network pair, wehere we obtained the reference distribution for their similarity. We then computed \(z\)-score and the corresponding empirical \(p\)-value. A network pair with \(p-\)value < 0.05 is considered significant (../source/network_overlap_randomisation.R)
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[1] "load the precomputed value from cache"Show code
# network similarity values
network_sim_df <- readRDS("../cache/network_jaccard_overlap_similarity_df.RDS") %>%
dplyr::filter(!grepl("core", V1), !grepl("core" , V2))
# edge counts
ecounts <- readRDS("../cache/network_complementarity_topological.RDS")%>%
dplyr::filter(property=="Number of edges") %>%
pull(value, name = network)
# compute minimum edge size for each pair
network_sim_df$min_ecount <- apply(network_sim_df, 1, function(x) min(ecounts[x[1]], ecounts[x[2]]))
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# process jaccard and overlap index
jaccard_index <- lapply(randomisation_overlap_result, function(x) x$intersect/x$union)
overlap_index <- lapply(1:length(randomisation_overlap_result), function(x)
randomisation_overlap_result[[x]][['intersect']]/network_sim_df$min_ecount[x])
# compute mean and sd, zscore and pvalue
network_sim_df <- network_sim_df %>%
mutate(jaccard.mean = sapply(jaccard_index, mean),
jaccard.sd = sapply(jaccard_index, sd),
overlap.mean = sapply(overlap_index, mean),
overlap.sd = sapply(overlap_index, sd),
jaccard.zscore = (jaccardIndex-jaccard.mean)/jaccard.sd,
jaccard.pval = pnorm(jaccard.zscore, lower.tail = F),
overlap.zscore = (overlapindex-overlap.mean)/overlap.sd,
overlap.pval = pnorm(overlap.zscore, lower.tail = F)
)
# label p values into classes
network_sim_df <- network_sim_df %>%
# make p values in groups
mutate(overlap.pval_level = cut(overlap.pval,
breaks = rev(c(1,5e-2, 1e-3, 1e-4, 1e-5, 0)),
labels = rev(c("ns","*","**","***","****")),
include.lowest = T, ordered_result = T),
# compute whether the pair are from both co-expression, or non co-expressions
type1 = grepl("coex", V1),
type2 = grepl("coex", V2),
pair_name = paste(str_remove(V1,"coex_|reactome_"),
str_remove(V2,"coex_|reactome_"), sep = " - "),
# label only if overlap score higher than 0.2
pair_label = ifelse(overlapindex > 0.2, pair_name, ""),
type_pair = factor(type1+type2, levels = 0:2, labels = c("non.coex - non.coex",
"coex - non.coex",
"coex - coex"))) %>%
mutate(overlap.pval_level = factor(overlap.pval_level, levels = rev(levels(overlap.pval_level))))
network_sim_df %>% count(overlap.pval_level) %>% paged_table()
Despite their wide range of similarity scores, we found that 955 out of 990 network pairs (96.5%) are significantly more similar than random expectation.
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# stable plot results
p_scatter = ggplot(network_sim_df, aes(x = overlapindex,
y = log2(overlapindex/overlap.mean))) +
geom_point(aes(col = overlap.pval_level)) +
scale_colour_viridis_d(direction = -1) +
theme_minimal() +
xlab(expression(Similarity~(S[AB]))) + labs(title = "Network pair similarity", col = "significance") +
ylab(expression(log[2](S[AB]/mu[S[AB]])))
# plot by type
p_scatter_by_type <- p_scatter + facet_grid(. ~ type_pair) + geom_text_repel(aes(label = pair_label))
p_scatter
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pval_lv_count_df <- network_sim_df %>% count(overlap.pval_level, name = "count")
pval_lv_count_by_type_df <- network_sim_df %>%
count(overlap.pval_level, type_pair, name = "count") %>%
group_by(type_pair) %>%
mutate(prop = count/sum(count))
p_count_by_type = ggplot(pval_lv_count_by_type_df, aes(x = overlap.pval_level, y = prop)) +
geom_col(aes(fill = overlap.pval_level)) +
scale_fill_viridis_d(direction = -1) +
xlab("Significance") + ylab("proportion") + guides(fill = F) +
theme_minimal() + facet_grid(. ~ type_pair) + labs(title = "Network similarity significance level")
p_scatter_by_type/p_count_by_type
Interestingly, we also observed that networks on different scales (i.e. among non co-expression layers) are all significantly similar, showing that there are key edges being maintained across genotype to phenotype.
Co-expression network characterisation
Core- and tissue-specific co-expression
We characterised our tissue-specific co-expression networks based on GTEx. Our hypothesis is that genes that are highly co-expression across all tissues are likely required for cellular developments and survival, and should show a strong correlation with of essentiality. In this analysis, we downloaded the list of human essential genes from the OGEE database (v2), also included in /data/OGEE_esential_genes_20190416.txt.
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## coexpresssion - share edges
## goal: to observe whether the shared edges among co-expression networks are essential
# 0 - load required data
## load coexel sum
library(pacman)
p_load(tidyverse, cowplot, knitr)
coex_el_sum_grouped = readRDS("../cache/coexpression_edge_counts_by_group.RDS")
# create a vector of total probability for each class
coex_el_sum_score = coex_el_sum_grouped %>%
ungroup() %>%
group_by(essential_edge_score) %>%
summarise(count = sum(n)) %>% pull(count, name = essential_edge_score)
coex_el_sum_grouped %>%
dplyr::rename(n_tissues = n_binned_relabel) %>%
group_by(n_tissues) %>%
summarise(n_edges = sum(n), percent = sum(n)*100/sum(coex_el_sum_score)) %>%
kable
| n_tissues | n_edges | percent |
|---|---|---|
| 0-5 | 12056143 | 91.8978690 |
| 5-10 | 747278 | 5.6961215 |
| 10-15 | 209979 | 1.6005635 |
| 15-20 | 70745 | 0.5392533 |
| 20-25 | 23372 | 0.1781529 |
| 25-30 | 7347 | 0.0560025 |
| 30-38 | 4203 | 0.0320373 |
The plot for higher essentiality aas number of genes increased is shown below.
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bar_essential = ggplot(coex_el_sum_grouped) +
geom_bar(aes(x = n_binned_relabel, y = n, fill = score), stat = "identity", position="fill") +
xlab("Number of tissues") + ylab("Edge proportion") + theme_cowplot() +
theme(legend.position = "bottom", axis.text.x = element_text(size = 10)) +
guides(fill = guide_legend(title = "Essential gene in edge")) + scale_fill_manual(values = c('grey60','#9ecae1','#3182bd'))
bar_essential
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#ggsave("./Figs/essentiality_co-expression.pdf", bar_essential, width = 4, height = 4)
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coex_el_sum = readRDS("../cache/coexpression_raw_edge_counts.RDS") %>% ungroup
coex_el_sum_by_tissue <- coex_el_sum %>%
count(n, essential_edge_score, name = "count")
OrphaNet Rare Disease association and analysis
Cross-scale network signatures of rare diseases
The structure of Orphanet Rare Disease Ontology was queried and processed using R interface of the Ontology Lookup Service (https://lgatto.github.io/rols/index.html). A number of calculation per-computed for further analyses on this section was performed in source/Orphanet_annotate_genes_to_ancestors.R.
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# load direct gene association
orpha_gene_onset_df <- readRDS("../cache/orpha_gene_onset_df.RDS")
# disease gene association with roots
orphanet_gene_association <- read_tsv("../data/orphaNet_disease_gene_association_with_roots.tsv")
# disease gene association at group level
source("../functions/readdata_functions.R")
gene_disease_orpha = process_disease_genes_data("../data/table_disease_gene_assoc_orphanet_genetic.tsv", 1, 2000)
[1] "read 28 diseases, of total 3593 associated genes."Show code
#source("../source/read_orphanet_gene_association_data.R")
gene_disease_orpha = gene_disease_orpha$disgene_df
# Modify and merge data
orpha_gene_display_df <- orphanet_gene_association %>%
dplyr::filter(n_genes > 0) %>%
mutate(ID = as.double(str_remove(orphaID, "Orphanet:")))
DT::datatable(orpha_gene_display_df[,c("ID", "label", "n_genes", "genes")] ,
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All"))))
Rare diseases are scarcely annotated, and most disease terms (2686 out of 3771) are only associated with one gene. Network-based measurements for individual diseases are unfeasible and grouping of the terms for higher level association are necessary.
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# from orphanet_mapping_top_branch
gene_per_disease = orpha_gene_onset_df %>%
dplyr::filter(!is.na(gene)) %>%
count(orphaID)
gene_per_disease_count = gene_per_disease %>%
mutate(group = cut(n, breaks = c(0:10, 100), labels = c(1:10, "> 10"))) %>%
count(group)
p = ggplot(gene_per_disease_count, aes(x = group, y =n)) + geom_col() +
ggtitle("Most orphanet diseases are immediately associated with one gene") +
theme_minimal() + ylab("Number of diseases") + xlab("Number of genes per disease")#+ scale_y_log10()
plotly::ggplotly(p)
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pacman::p_load(cowplot)
#gene_per_disease_group = gene_disease_orpha %>%
# mutate(group = cut(n_genes, breaks = c(seq(0,100,20), seq(200,1000, 200), Inf))) %>% count(group)
#ggplot(gene_per_disease_group, aes(x = group, y =n)) + geom_col() +
# ggtitle("Most orphanet diseases are immediately associated with one gene") +
# theme_minimal() + ylab("Number of diseases") + xlab("Number of genes per disease")#+ scale_y_log10()
# number shift
gene_per_disease_group = gene_disease_orpha %>%
mutate(disease = "Grouped", n = N) %>%
select(disease, n)
gene_per_disease = gene_per_disease %>%
mutate(disease = "Individual")
gene_per_disease_both = gene_per_disease %>%
select(disease, n) %>%
bind_rows(., gene_per_disease_group) %>%
dplyr::filter(n>0) %>%
# relevel factor
mutate(disease = factor(disease, levels = c("Individual","Grouped")))
# add break values for plotting on log scale on x axis
breakvals = c(0:9, seq(10,90,10), seq(100,1000,100), 2000)
gene_per_disease_both_count = gene_per_disease_both %>%
mutate(group = cut(n, breaks = breakvals, include.lowest = T, labels = breakvals[-1])) %>%
group_by(disease, group) %>%
summarise(n = n()) %>%
mutate(prop = n/sum(n)) %>%
full_join(., tibble(group = as_factor(breakvals[-1])))
gene_per_disease_both_count$disease[is.na(gene_per_disease_both_count$disease)] = "Grouped"
gene_per_disease_both_count$n[is.na(gene_per_disease_both_count$n)] = 0
# plot
#ggplot(gene_per_disease_both_count, aes(y = prop, x = group)) + geom_col() + theme_minimal() +facet_grid(disease ~ .)
p = ggplot(gene_per_disease_both_count, aes(y = n, x = group)) +
geom_col() +
facet_grid(disease ~ ., scales = "free") +
#scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +
scale_x_discrete(breaks = c(1,10,100,1000))+
geom_vline(xintercept = which(levels(gene_per_disease_both_count$group)==20), linetype = "dashed", col = "red") + theme_cowplot() + xlab("Genes per disease term") + ylab("Number of terms")
p
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#ggsave("../Figs/orphanet_individual_vs_grouped_diseases_bar.pdf", p, width = 3*1.5, height = 2*1.5)
Based on the plot above, accumulating gene-disease association for descendant disease terms of ‘Rare genetic disease’ ( “Orphanet:98053”) resulting in physiologically distinct disease groups where the majority (26 out of 28) groups are associated with sufficient amount of genes for module detection (\(n=20\)).
The disease gene association can be found in data/table_disease_gene_assoc_orphanet_genetic.tsv. The summary of disease groups and all associated genes are shown below.
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rmarkdown::paged_table(gene_disease_orpha %>% select(name, N) %>% arrange(-N))
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# roots that are removed
removed_roots <- gene_disease_orpha %>% filter(N<20) %>% pull(name)
# table for removed terms
removed_disease_terms <- orphanet_gene_association %>%
mutate(n_roots = str_count(roots, ";")+1,
removed_roots = grepl(removed_roots[1], roots) + grepl(removed_roots[2], roots),
remained_roots = n_roots - removed_roots) %>%
filter(remained_roots == 0) %>%
select(orphaID, label, genes, roots)
removed_genes <- sapply(removed_disease_terms$genes, function(x) str_split(x, ";")) %>% unlist %>% unique()
knitr::kable(removed_disease_terms)
| orphaID | label | genes | roots |
|---|---|---|---|
| Orphanet:199241 | Pulmonary capillary hemangiomatosis | EIF2AK4 | Rare genetic respiratory disease |
| Orphanet:210122 | Congenital alveolar capillary dysplasia | FOXF1 | Rare genetic respiratory disease |
| Orphanet:217566 | Chronic respiratory distress with surfactant metabolism deficiency | SFTPC | Rare genetic respiratory disease |
| Orphanet:217563 | Neonatal acute respiratory distress due to SP-B deficiency | SFTPB | Rare genetic respiratory disease |
| Orphanet:440402 | Interstitial lung disease due to ABCA3 deficiency | ABCA3 | Rare genetic respiratory disease |
| Orphanet:440392 | Interstitial lung disease due to SP-C deficiency | SFTPC | Rare genetic respiratory disease |
| Orphanet:444092 | Autoimmune interstitial lung disease-arthritis syndrome | COPA | Rare genetic respiratory disease |
| Orphanet:31837 | Pulmonary venoocclusive disease | BMPR2;EIF2AK4 | Rare genetic respiratory disease |
| Orphanet:100051 | Hereditary angioedema type 2 | SERPING1 | Serpinopathy |
| Orphanet:100050 | Hereditary angioedema type 1 | SERPING1 | Serpinopathy |
There are 10 disease terms whose associated genes are not associated with any other disease groups, and hence these 8 genes are omitted.
Tree map for ontology representation of rare diseases
Show code
# download the association
library(RColorBrewer)
orphanet_gene_association_unique_root <- orphanet_gene_association %>%
separate_rows(., roots, sep = ";", convert = T) %>% dplyr::filter(!is.na(roots)) %>%
mutate(roots = as.factor(roots))
## take a function to allow modifying alpha values
# Add an alpha value to a colour
add.alpha <- function(col=NULL, alpha=1){
if(missing(col))
stop("Please provide a vector of colours.")
apply(sapply(col, col2rgb)/255, 2,
function(x)
rgb(x[1], x[2], x[3], alpha=alpha))
}
# define colours for all disease groups
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nrow(gene_disease_orpha))
# add id 1-28
gene_disease_orpha$id = 1:nrow(gene_disease_orpha)
# add colour with corresponding alpha values to each disease group
gene_disease_orpha_mod <- gene_disease_orpha %>%
rowwise() %>%
mutate(col =mycolors[id])
#mutate(col = add.alpha(mycolors[id], N/max(gene_disease_orpha$N)))
gene_disease_orpha_mod$root = "Orphanet"
# load voronoi treemap package
if(!"voronoiTreemap" %in% rownames(installed.packages())){
pacman::p_load_gh("https://github.com/uRosConf/voronoiTreemap")
} else{
library(voronoiTreemap)
}
gene_disease_orpha_mod$plotlab = str_remove_all( gene_disease_orpha_mod$name, "Rare |genetic | disease| syndrome| disorder")
onto_json <- vt_export_json(vt_input_from_df(gene_disease_orpha_mod, hierachyVar0 = "root", hierachyVar1 = "name", hierachyVar2 = "name", colorVar = "col", weightVar = "N", labelVar = "plotlab"))
vt_d3(onto_json)
Patients level prioritization
In clinical settings, an approximate number of X potential causal variants are obtained after a rare disease patient underwent exome sequencing. With additional stringent pathogenecity evaluation, x% of those variants are filtered out. Yet, there remains 100-x% which regarded as high confidence [cite]. We earlier showed that our informed multiplex propagation is a powerful tool for improving the retrieval rate of disease causal genes. We propose that it can also act as an additional evaluation metric for patients’ variant prioritization. As a test scenario, we obtained variant lists from five rare disease patients with neurological symptoms. The causal variant for each patient has already been validated and we can therefore use them for benchmarking.
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| SYMBOL | CHROM | POS | score | ID | Causative |
|---|---|---|---|---|---|
| TAS2R43 | 12 | 11244734 | 6.318 | ENST00000531678.1:c.95C>G | NO |
| SULT1A1 | 16 | 28617494 | 11.270 | ENST00000395609.1:c.658G>A | NO |
| ZNF414 | 19 | 8575770 | 9.448 | ENST00000393927.4:c.1064C>T | NO |
| JADE3 | X | 46918243 | 16.040 | ENST00000218343.4:c.2236T>C | NO |
| CCDC22 | X | 49105278 | 14.930 | ENST00000376227.3:c.1432A>C | NO |
| HUWE1 | X | 53561062 | 32.000 | ENST00000342160.3:c.12928G>C | YES |
| FAAH2 | X | 57515286 | 17.560 | ENST00000374900.4:c.1520T>G | NO |
| ABCB7 | X | 74291377 | 15.380 | ENST00000253577.3:c.1177A>G | NO |
| GPR119 | X | 129518716 | 21.500 | ENST00000276218.2:c.706C>G | NO |
This patient are diagnosed, i.e. has a confirmed causal variant. We wonder whether the causal gene can be picked up by tha algorithm. The patient are presented with symptoms belonging to rare genetic neurological disorder, and we used the 10-fold CV retrieval result for this disease group to rank the genes based on their average visiting probability. We incorporated additional columns to the variant sets: the average visiting probability (from 10-fold CV), the corresponding global rank (from the entire genome) and the local rank (rank of the gene for the paitent).
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disease_group <- "Rare genetic neurological disorder"
# result from using significant networks
rank_df_all_folds <- readRDS("../cache/LCC_CV_ranking_rare_genetic_diseases_no_LCC_recompute.RDS")
combn_visiting_prob <- list()
for(i in names(rank_df_all_folds)){
combn_visiting_prob[[i]] <- rank_df_all_folds[[i]] %>%
bind_rows() %>%
group_by(GeneName, trueset) %>%
summarise(avg_prob = mean(avg), .groups = "drop") %>%
ungroup() %>%
arrange(-avg_prob) %>%
mutate(rank = 1:n())
}
rank_df_disease <- combn_visiting_prob[[disease_group]]
variants_annotated <- left_join(variants, rank_df_disease, by = c("SYMBOL"="GeneName")) %>%
select(-trueset) %>%
rename(GlobalRank = rank) %>%
arrange(GlobalRank) %>%
mutate(LocalRank = 1:n())
knitr::kable(variants_annotated)
| SYMBOL | CHROM | POS | score | ID | Causative | avg_prob | GlobalRank | LocalRank |
|---|---|---|---|---|---|---|---|---|
| HUWE1 | X | 53561062 | 32.000 | ENST00000342160.3:c.12928G>C | YES | 5e-07 | 208 | 1 |
| CCDC22 | X | 49105278 | 14.930 | ENST00000376227.3:c.1432A>C | NO | 2e-07 | 1919 | 2 |
| ABCB7 | X | 74291377 | 15.380 | ENST00000253577.3:c.1177A>G | NO | 2e-07 | 2598 | 3 |
| ZNF414 | 19 | 8575770 | 9.448 | ENST00000393927.4:c.1064C>T | NO | 2e-07 | 4861 | 4 |
| JADE3 | X | 46918243 | 16.040 | ENST00000218343.4:c.2236T>C | NO | 1e-07 | 8222 | 5 |
| FAAH2 | X | 57515286 | 17.560 | ENST00000374900.4:c.1520T>G | NO | 0e+00 | 11710 | 6 |
| SULT1A1 | 16 | 28617494 | 11.270 | ENST00000395609.1:c.658G>A | NO | 0e+00 | 12267 | 7 |
| GPR119 | X | 129518716 | 21.500 | ENST00000276218.2:c.706C>G | NO | 0e+00 | 17956 | 8 |
| TAS2R43 | 12 | 11244734 | 6.318 | ENST00000531678.1:c.95C>G | NO | 0e+00 | 20124 | 9 |
We observed our algorithm picked a variant on HUWE1, which was confirmed to be the causal variant.